It's All About Your Sketch: Democratising Sketch Control in Diffusion Models

Subhadeep Koley, Ayan Kumar Bhunia, Deeptanshu Sekhri, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 7204-7214

This paper unravels the potential of sketches for diffusion models addressing the deceptive promise of direct sketch control in generative AI. We importantly democratise the process enabling amateur sketches to generate precise images living up to the commitment of "what you sketch is what you get". A pilot study underscores the necessity revealing that deformities in existing models stem from spatial-conditioning. To rectify this we propose an abstraction-aware framework utilising a sketch adapter adaptive time-step sampling and discriminative guidance from a pre-trained fine-grained sketch-based image retrieval model working synergistically to reinforce fine-grained sketch-photo association. Our approach operates seamlessly during inference without the need for textual prompts; a simple rough sketch akin to what you and I can create suffices! We welcome everyone to examine results presented in the paper and its supplementary. Contributions include democratising sketch control introducing an abstraction-aware framework and leveraging discriminative guidance validated through extensive experiments.